{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用Trainer和Tester快速训练和测试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读入和处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/remote-home/ynzheng/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/io/loader/classification.py:340: UserWarning: SST2's test file has no target.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In total 3 datasets:\n",
      "\ttest has 1821 instances.\n",
      "\ttrain has 67349 instances.\n",
      "\tdev has 872 instances.\n",
      "In total 2 vocabs:\n",
      "\twords has 16292 entries.\n",
      "\ttarget has 2 entries.\n",
      "\n",
      "+-----------------------------------+--------+-----------------------------------+---------+\n",
      "| raw_words                         | target | words                             | seq_len |\n",
      "+-----------------------------------+--------+-----------------------------------+---------+\n",
      "| hide new secretions from the p... | 1      | [4110, 97, 12009, 39, 2, 6843,... | 7       |\n",
      "+-----------------------------------+--------+-----------------------------------+---------+\n",
      "Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n"
     ]
    }
   ],
   "source": [
    "from fastNLP.io import SST2Pipe\n",
    "\n",
    "pipe = SST2Pipe()\n",
    "databundle = pipe.process_from_file()\n",
    "vocab = databundle.get_vocab('words')\n",
    "print(databundle)\n",
    "print(databundle.get_dataset('train')[0])\n",
    "print(databundle.get_vocab('words'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4925 872 75\n"
     ]
    }
   ],
   "source": [
    "train_data = databundle.get_dataset('train')[:5000]\n",
    "train_data, test_data = train_data.split(0.015)\n",
    "dev_data = databundle.get_dataset('dev')\n",
    "print(len(train_data),len(dev_data),len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-----------+--------+-------+---------+\n",
      "| field_names | raw_words | target | words | seq_len |\n",
      "+-------------+-----------+--------+-------+---------+\n",
      "|   is_input  |   False   | False  |  True |   True  |\n",
      "|  is_target  |   False   |  True  | False |  False  |\n",
      "| ignore_type |           | False  | False |  False  |\n",
      "|  pad_value  |           |   0    |   0   |    0    |\n",
      "+-------------+-----------+--------+-------+---------+\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prettytable.PrettyTable at 0x7f0db03d0640>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.print_field_meta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import AccuracyMetric\n",
    "from fastNLP import Const\n",
    "\n",
    "# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n",
    "metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataSetIter初探"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_x:  {'words': tensor([[   13,   830,  7746,   174,     3,    47,     6,    83,  5752,    15,\n",
      "          2177,    15,    63,    57,   406,    84,  1009,  4973,    27,    17,\n",
      "         13785,     3,   533,  3687, 15623,    39,   375,     8, 15624,     8,\n",
      "          1323,  4398,     7],\n",
      "        [ 1045, 11113,    16,   104,     5,     4,   176,  1824,  1704,     3,\n",
      "             2,    18,    11,     4,  1018,   432,   143,    33,   245,   308,\n",
      "             7,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0]]), 'seq_len': tensor([33, 21])}\n",
      "batch_y:  {'target': tensor([1, 0])}\n",
      "batch_x:  {'words': tensor([[  14,   10,    4,  311,    5,  154, 1418,  609,    7],\n",
      "        [  14,   10,  437,   32,   78,    3,   78,  437,    7]]), 'seq_len': tensor([9, 9])}\n",
      "batch_y:  {'target': tensor([0, 1])}\n",
      "batch_x:  {'words': tensor([[    4,   277,   685,    18,     7],\n",
      "        [15618,  3204,     5,  1675,     0]]), 'seq_len': tensor([5, 4])}\n",
      "batch_y:  {'target': tensor([1, 1])}\n",
      "batch_x:  {'words': tensor([[    2,   155,     3,  4426,     3,   239,     3,   739,     5,  1136,\n",
      "            41,    43,  2427,   736,     2,   648,    10, 15620,  2285,     7],\n",
      "        [   24,    95,    28,    46,     8,   336,    38,   239,     8,  2133,\n",
      "             2,    18,    10, 15622,  1421,     6,    61,     5,   387,     7]]), 'seq_len': tensor([20, 20])}\n",
      "batch_y:  {'target': tensor([0, 0])}\n",
      "batch_x:  {'words': tensor([[  879,    96,     8,  1026,    12,  8067,    11, 13623,     8, 15619,\n",
      "             4,   673,   662,    15,     4,  1154,   240,   639,   417,     7],\n",
      "        [   45,   752,   327,   180,    10, 15621,    16,    72,  8904,     9,\n",
      "          1217,     7,     0,     0,     0,     0,     0,     0,     0,     0]]), 'seq_len': tensor([20, 12])}\n",
      "batch_y:  {'target': tensor([0, 1])}\n"
     ]
    }
   ],
   "source": [
    "from fastNLP import BucketSampler\n",
    "from fastNLP import DataSetIter\n",
    "\n",
    "tmp_data = dev_data[:10]\n",
    "# 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
    "# 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
    "sampler = BucketSampler(batch_size=2, seq_len_field_name='seq_len')\n",
    "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
    "for batch_x, batch_y in batch:\n",
    "    print(\"batch_x: \",batch_x)\n",
    "    print(\"batch_y: \", batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_x:  {'words': tensor([[   13,   830,  7746,   174,     3,    47,     6,    83,  5752,    15,\n",
      "          2177,    15,    63,    57,   406,    84,  1009,  4973,    27,    17,\n",
      "         13785,     3,   533,  3687, 15623,    39,   375,     8, 15624,     8,\n",
      "          1323,  4398,     7],\n",
      "        [ 1045, 11113,    16,   104,     5,     4,   176,  1824,  1704,     3,\n",
      "             2,    18,    11,     4,  1018,   432,   143,    33,   245,   308,\n",
      "             7,    -1,    -1,    -1,    -1,    -1,    -1,    -1,    -1,    -1,\n",
      "            -1,    -1,    -1]]), 'seq_len': tensor([33, 21])}\n",
      "batch_y:  {'target': tensor([1, 0])}\n",
      "batch_x:  {'words': tensor([[  14,   10,    4,  311,    5,  154, 1418,  609,    7],\n",
      "        [  14,   10,  437,   32,   78,    3,   78,  437,    7]]), 'seq_len': tensor([9, 9])}\n",
      "batch_y:  {'target': tensor([0, 1])}\n",
      "batch_x:  {'words': tensor([[    2,   155,     3,  4426,     3,   239,     3,   739,     5,  1136,\n",
      "            41,    43,  2427,   736,     2,   648,    10, 15620,  2285,     7],\n",
      "        [   24,    95,    28,    46,     8,   336,    38,   239,     8,  2133,\n",
      "             2,    18,    10, 15622,  1421,     6,    61,     5,   387,     7]]), 'seq_len': tensor([20, 20])}\n",
      "batch_y:  {'target': tensor([0, 0])}\n",
      "batch_x:  {'words': tensor([[    4,   277,   685,    18,     7],\n",
      "        [15618,  3204,     5,  1675,    -1]]), 'seq_len': tensor([5, 4])}\n",
      "batch_y:  {'target': tensor([1, 1])}\n",
      "batch_x:  {'words': tensor([[  879,    96,     8,  1026,    12,  8067,    11, 13623,     8, 15619,\n",
      "             4,   673,   662,    15,     4,  1154,   240,   639,   417,     7],\n",
      "        [   45,   752,   327,   180,    10, 15621,    16,    72,  8904,     9,\n",
      "          1217,     7,    -1,    -1,    -1,    -1,    -1,    -1,    -1,    -1]]), 'seq_len': tensor([20, 12])}\n",
      "batch_y:  {'target': tensor([0, 1])}\n"
     ]
    }
   ],
   "source": [
    "tmp_data.set_pad_val('words',-1)\n",
    "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
    "for batch_x, batch_y in batch:\n",
    "    print(\"batch_x: \",batch_x)\n",
    "    print(\"batch_y: \", batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_x:  {'words': tensor([[   45,   752,   327,   180,    10, 15621,    16,    72,  8904,     9,\n",
      "          1217,     7,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0],\n",
      "        [  879,    96,     8,  1026,    12,  8067,    11, 13623,     8, 15619,\n",
      "             4,   673,   662,    15,     4,  1154,   240,   639,   417,     7,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'seq_len': tensor([12, 20])}\n",
      "batch_y:  {'target': tensor([1, 0])}\n",
      "batch_x:  {'words': tensor([[   13,   830,  7746,   174,     3,    47,     6,    83,  5752,    15,\n",
      "          2177,    15,    63,    57,   406,    84,  1009,  4973,    27,    17,\n",
      "         13785,     3,   533,  3687, 15623,    39,   375,     8, 15624,     8,\n",
      "          1323,  4398,     7,     0,     0,     0,     0,     0,     0,     0],\n",
      "        [ 1045, 11113,    16,   104,     5,     4,   176,  1824,  1704,     3,\n",
      "             2,    18,    11,     4,  1018,   432,   143,    33,   245,   308,\n",
      "             7,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'seq_len': tensor([33, 21])}\n",
      "batch_y:  {'target': tensor([1, 0])}\n",
      "batch_x:  {'words': tensor([[  14,   10,    4,  311,    5,  154, 1418,  609,    7,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0],\n",
      "        [  14,   10,  437,   32,   78,    3,   78,  437,    7,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
      "            0,    0,    0,    0]]), 'seq_len': tensor([9, 9])}\n",
      "batch_y:  {'target': tensor([0, 1])}\n",
      "batch_x:  {'words': tensor([[    2,   155,     3,  4426,     3,   239,     3,   739,     5,  1136,\n",
      "            41,    43,  2427,   736,     2,   648,    10, 15620,  2285,     7,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0],\n",
      "        [   24,    95,    28,    46,     8,   336,    38,   239,     8,  2133,\n",
      "             2,    18,    10, 15622,  1421,     6,    61,     5,   387,     7,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'seq_len': tensor([20, 20])}\n",
      "batch_y:  {'target': tensor([0, 0])}\n",
      "batch_x:  {'words': tensor([[    4,   277,   685,    18,     7,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0],\n",
      "        [15618,  3204,     5,  1675,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0]]), 'seq_len': tensor([5, 4])}\n",
      "batch_y:  {'target': tensor([1, 1])}\n"
     ]
    }
   ],
   "source": [
    "from fastNLP.core.field import Padder\n",
    "import numpy as np\n",
    "class FixLengthPadder(Padder):\n",
    "    def __init__(self, pad_val=0, length=None):\n",
    "        super().__init__(pad_val=pad_val)\n",
    "        self.length = length\n",
    "        assert self.length is not None, \"Creating FixLengthPadder with no specific length!\"\n",
    "\n",
    "    def __call__(self, contents, field_name, field_ele_dtype, dim):\n",
    "        #计算当前contents中的最大长度\n",
    "        max_len = max(map(len, contents))\n",
    "        #如果当前contents中的最大长度大于指定的padder length的话就报错\n",
    "        assert max_len <= self.length, \"Fixed padder length smaller than actual length! with length {}\".format(max_len)\n",
    "        array = np.full((len(contents), self.length), self.pad_val, dtype=field_ele_dtype)\n",
    "        for i, content_i in enumerate(contents):\n",
    "            array[i, :len(content_i)] = content_i\n",
    "        return array\n",
    "\n",
    "#设定FixLengthPadder的固定长度为40\n",
    "tmp_padder = FixLengthPadder(pad_val=0,length=40)\n",
    "#利用dataset的set_padder函数设定words field的padder\n",
    "tmp_data.set_padder('words',tmp_padder)\n",
    "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
    "for batch_x, batch_y in batch:\n",
    "    print(\"batch_x: \",batch_x)\n",
    "    print(\"batch_y: \", batch_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用DataSetIter自己编写训练过程\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----start training-----\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 2.68 seconds!\n",
      "Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.38 seconds!\n",
      "Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.51 seconds!\n",
      "Epoch 2 Avg Loss: 0.16 AccuracyMetric: acc=0.747706 70268ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.96 seconds!\n",
      "Epoch 3 Avg Loss: 0.06 AccuracyMetric: acc=0.741972 90349ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 1.04 seconds!\n",
      "Epoch 4 Avg Loss: 0.03 AccuracyMetric: acc=0.740826 114250ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.8 seconds!\n",
      "Epoch 5 Avg Loss: 0.02 AccuracyMetric: acc=0.738532 134742ms\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.65 seconds!\n",
      "Epoch 6 Avg Loss: 0.01 AccuracyMetric: acc=0.731651 154503ms\n"
     ]
    },
    {
     "data": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.8 seconds!\n",
      "Epoch 7 Avg Loss: 0.01 AccuracyMetric: acc=0.738532 175397ms\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.36 seconds!\n",
      "Epoch 8 Avg Loss: 0.01 AccuracyMetric: acc=0.733945 192384ms\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.84 seconds!\n",
      "Epoch 9 Avg Loss: 0.01 AccuracyMetric: acc=0.744266 214417ms\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Evaluate data in 0.04 seconds!\n",
      "[tester] \n",
      "AccuracyMetric: acc=0.786667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'AccuracyMetric': {'acc': 0.786667}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fastNLP import BucketSampler\n",
    "from fastNLP import DataSetIter\n",
    "from fastNLP.models import CNNText\n",
    "from fastNLP import Tester\n",
    "import torch\n",
    "import time\n",
    "\n",
    "embed_dim = 100\n",
    "model = CNNText((len(vocab),embed_dim), num_classes=2, dropout=0.1)\n",
    "\n",
    "def train(epoch, data, devdata):\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "    lossfunc = torch.nn.CrossEntropyLoss()\n",
    "    batch_size = 32\n",
    "\n",
    "    # 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
    "    # 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
    "    train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len')\n",
    "    train_batch = DataSetIter(batch_size=batch_size, dataset=data, sampler=train_sampler)\n",
    "\n",
    "    start_time = time.time()\n",
    "    print(\"-\"*5+\"start training\"+\"-\"*5)\n",
    "    for i in range(epoch):\n",
    "        loss_list = []\n",
    "        for batch_x, batch_y in train_batch:\n",
    "            optimizer.zero_grad()\n",
    "            output = model(batch_x['words'])\n",
    "            loss = lossfunc(output['pred'], batch_y['target'])\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_list.append(loss.item())\n",
    "\n",
    "        #这里verbose如果为0，在调用Tester对象的test()函数时不输出任何信息，返回评估信息; 如果为1，打印出验证结果，返回评估信息\n",
    "        #在调用过Tester对象的test()函数后，调用其_format_eval_results(res)函数，结构化输出验证结果\n",
    "        tester_tmp = Tester(devdata, model, metrics=AccuracyMetric(), verbose=0)\n",
    "        res=tester_tmp.test()\n",
    "\n",
    "        print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=\" \")\n",
    "        print(tester_tmp._format_eval_results(res),end=\" \")\n",
    "        print('{:d}ms'.format(round((time.time()-start_time)*1000)))\n",
    "        loss_list.clear()\n",
    "\n",
    "train(10, train_data, dev_data)\n",
    "#使用tester进行快速测试\n",
    "tester = Tester(test_data, model, metrics=AccuracyMetric())\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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